1 Setup

suppressPackageStartupMessages({
  library(data.table)
  library(DESeq2)
  library(gplots)
  library(here)
  library(hyperSpec)
  library(parallel)
  library(pander)
  library(plotly)
  library(tidyverse)
  library(tximport)
  library(vsn)
})
source(here("UPSCb-common/src/R/featureSelection.R"))
hpal <- colorRampPalette(c("blue","white","red"))(100)
samples <- read_csv(here("doc/samples_final.csv"))
## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   ScilifeID = col_character(),
##   SubmittedID = col_character(),
##   Stages = col_character(),
##   Description = col_character(),
##   ID = col_character()
## )

2 Raw data

filelist <- list.files(here("data/salmon"), 
                          recursive = TRUE, 
                          pattern = "quant.sf",
                          full.names = TRUE)

Sanity check to ensure that the data is sorted according to the sample info

filelist <- filelist[match(samples$ScilifeID,sub("_sortmerna.*","",basename(dirname(filelist))))]

stopifnot(all(match(sub("_sortmerna.*","",basename(dirname(filelist))),
                    samples$ScilifeID) == 1:nrow(samples)))

name the file list vector

names(filelist) <- samples$ID

Read the expression at the gene level

counts <- suppressMessages(round(tximport(files = filelist, 
                                          type = "salmon",
                                          txOut=TRUE)$counts))

combine technical replicates

samples$ID <- sub("_L00[1,2]", "",
                  samples$ScilifeID)
counts <- do.call(
  cbind,
  lapply(split.data.frame(t(counts),
                          samples$ID),
         colSums))

csamples <- samples[,-1]
csamples <- csamples[match(colnames(counts),csamples$ID),]

2.1 Quality Control

  • Check how many genes are never expressed
sel <- rowSums(counts) == 0
sprintf("%s%% percent (%s) of %s genes are not expressed",
        round(sum(sel) * 100/ nrow(counts),digits=1),
        sum(sel),
        nrow(counts))
## [1] "19.8% percent (13135) of 66360 genes are not expressed"
  • Let us take a look at the sequencing depth, colouring by Stages.
dat <- tibble(x=colnames(counts),y=colSums(counts)) %>% 
  bind_cols(csamples)

ggplot(dat,aes(x,y,fill=csamples$Stages)) + geom_col() + 
  scale_y_continuous(name="reads") +
  theme(axis.text.x=element_text(angle=90,size=4),axis.title.x=element_blank())

  • Display the per-gene mean expression

i.e. the mean raw count of every gene across samples is calculated and displayed on a log10 scale.

The cumulative gene coverage is as expected

ggplot(data.frame(value=log10(rowMeans(counts))),aes(x=value)) + 
  geom_density() + ggtitle("gene mean raw counts distribution") +
  scale_x_continuous(name="mean raw counts (log10)")
## Warning: Removed 13135 rows containing non-finite values (stat_density).

The same is done for the individual samples colored by Stages.

dat <- as.data.frame(log10(counts)) %>% utils::stack() %>% 
  mutate(Stages=csamples$Stages[match(ind,csamples$ID)])

ggplot(dat,aes(x=values,group=ind,col=Stages)) + 
  geom_density() + ggtitle("sample raw counts distribution") +
  scale_x_continuous(name="per gene raw counts (log10)")
## Warning: Removed 622137 rows containing non-finite values (stat_density).

2.2 Export

dir.create(here("data/analysis/salmon"),showWarnings=FALSE,recursive=TRUE)
write.csv(counts,file=here("data/analysis/salmon/raw-unormalised-gene-expression_data_genes+TEs.csv"))

3 Data normalisation

3.1 Preparation

For visualization, the data is submitted to a variance stabilization transformation using DESeq2. The dispersion is estimated independently of the sample tissue and replicate.

csamples$Stages <- factor(csamples$Stages)

dds <- DESeqDataSetFromMatrix(
  countData = counts,
  colData = csamples,
  design = ~Stages)
## converting counts to integer mode
save(dds,file=here("data/analysis/salmon/dds_genes_TEs.rda"))

Check the size factors (i.e. the sequencing library size effect)

dds <- estimateSizeFactors(dds)
sizes <- sizeFactors(dds)
pander(sizes)
Table continues below
P7614_301_S1 P7614_302_S2 P7614_303_S3 P7614_304_S4 P7614_305_S5
0.997 0.9789 1.096 1.053 1.102
Table continues below
P7614_306_S6 P7614_307_S7 P7614_308_S8 P7614_309_S9 P7614_310_S10
1.311 1.091 1.167 0.9277 0.8292
Table continues below
P7614_311_S11 P7614_312_S12 P7614_313_S13 P7614_314_S14 P7614_315_S15
0.918 0.8462 1.007 0.9453 1.102
Table continues below
P7614_316_S16 P7614_317_S17 P7614_318_S18 P7614_319_S19 P7614_320_S20
1.01 0.9052 0.9999 0.9703 0.9241
P7614_321_S21 P7614_322_S22 P7614_323_S23 P7614_324_S24
1.119 1.072 0.9857 0.9988
boxplot(sizes, main="Sequencing libraries size factor")

3.2 Variance Stabilising Transformation

vsd <- varianceStabilizingTransformation(dds, blind=TRUE)
vst <- assay(vsd)
vst <- vst - min(vst)
save(vst,file=here("data/analysis/DE/vst-blind_genes_TEs.rda"))

3.3 Variance Stabilising Transformation

vsda <- varianceStabilizingTransformation(dds, blind=FALSE)
vsta <- assay(vsda)
vsta <- vsta - min(vsta)
save(vsta,file=here("data/analysis/DE/vst-aware_genes_TEs.rda"))

# prepare the data to build the network
#ID <- rownames(vsta)
#vsta <- cbind(ID,vsta)
#vsta_tibble <- as_tibble(vsta)
#write_tsv(vsta_tibble,path=here("data/analysis/DE/vst-aware_genes+TEs.tsv"))
  • Validation

The variance stabilisation worked adequately

meanSdPlot(vsta[rowSums(vsta)>0,])

3.4 QC on the normalised data

3.4.1 PCA

load(here("data/analysis/salmon/dds_genes_TEs.rda"))
load(here("data/analysis/DE/vst-aware_genes_TEs.rda"))
pc <- prcomp(t(vsta))
percent <- round(summary(pc)$importance[2,]*100)
  • Cumulative components effect

We define the number of variable of the model

nvar=1

An the number of possible combinations

nlevel=nlevels(dds$Stages) 

We plot the percentage explained by the different components, the red line represent the number of variable in the model, the orange line the number of variable combinations.

ggplot(tibble(x=1:length(percent),y=cumsum(percent)),aes(x=x,y=y)) +
  geom_line() + scale_y_continuous("variance explained (%)",limits=c(0,100)) +
  scale_x_continuous("Principal component") + 
  geom_vline(xintercept=nvar,colour="red",linetype="dashed",size=0.5) + 
  geom_hline(yintercept=cumsum(percent)[nvar],colour="red",linetype="dashed",size=0.5) +
  geom_vline(xintercept=nlevel,colour="orange",linetype="dashed",size=0.5) + 
  geom_hline(yintercept=cumsum(percent)[nlevel],colour="orange",linetype="dashed",size=0.5)

3.4.2 2D

pc.dat <- bind_cols(PC1=pc$x[,1],
                    PC2=pc$x[,2],
                    csamples)

p <- ggplot(pc.dat,aes(x=PC2,y=PC1,col=Stages,text=dds$ID)) + 
  geom_point(size=2) + 
  ggtitle("Principal Component Analysis")
          #,subtitle="variance stabilized counts") 

plot(p + labs(x=paste("PC2 (",percent[2],"%)",sep=""),
              y=paste("PC1 (",percent[1],"%)",sep="")))

ggplotly(p) %>% 
  layout(xaxis=list(title=paste("PC2 (",percent[2],"%)",sep="")),
         yaxis=list(title=paste("PC1 (",percent[1],"%)",sep="")))

3.4.3 Heatmap

Filter for noise

conds <- factor(csamples$Stages)
sels <- rangeFeatureSelect(counts=vsta,
                           conditions=conds,
                           nrep=3)
## Warning in xy.coords(x, y, xlabel, ylabel, log): 1 y value <= 0 omitted from
## logarithmic plot

vst.cutoff <- 1
  • Heatmap of “all” genes
hm <- heatmap.2(t(scale(t(vsta[sels[[vst.cutoff+1]],]))),
          distfun=pearson.dist,
          hclustfun=function(X){hclust(X,method="ward.D2")},
          labRow = NA,trace = "none",
          labCol = conds,
          col=hpal)

plot(as.hclust(hm$colDendrogram),xlab="",sub="",labels=conds)

  • Biological QA only on TEs
#load(here("data/analysis/DE/vst-aware_genes_TEs.rda"))
TEs <- vsta[grepl("^MA_", rownames(vsta)) == FALSE, ]

4 PCA of only TEs (subsetted data)

pc_TEs <- prcomp(t(vsta[grepl("^MA_", rownames(vsta)) == FALSE, ]))
percent_TEs <- round(summary(pc_TEs)$importance[2,]*100)

4.1 2D

pc.dat_TEs <- bind_cols(PC1=pc_TEs$x[,1],
                    PC2=pc_TEs$x[,2],
                    csamples)

p <- ggplot(pc.dat_TEs,aes(x=PC1,y=PC2,col=Stages,text=dds$ID)) + 
  geom_point(size=2) + 
  ggtitle("Principal Component Analysis",subtitle="variance stabilized counts")

ggplotly(p) %>% 
  layout(xaxis=list(title=paste("PC1 (",percent_TEs[1],"%)",sep="")),
         yaxis=list(title=paste("PC2 (",percent_TEs[2],"%)",sep="")))

4.1.1 Heatmap

Filter for noise

conds_TEs <- factor(csamples$Stages)
sels_TEs <- rangeFeatureSelect(counts=TEs,
                           conditions=conds_TEs,
                           nrep=3)

vst.cutoff <- 1
  • Heatmap of “all” genes
hm <- heatmap.2(t(scale(t(TEs[sels_TEs[[vst.cutoff+1]],]))),
                distfun=pearson.dist,
                hclustfun=function(X){hclust(X,method="ward.D2")},
                labRow = NA,trace = "none",
                labCol = conds_TEs,
                col=hpal)

plot(as.hclust(hm$colDendrogram),xlab="",sub="",labels=conds_TEs)

hm2 <- heatmap.2(TEs, 
          scale = "row", 
          labRow = NULL, 
          labCol = conds_TEs,
          trace = "none",
          col=hpal)

plot(as.hclust(hm2$colDendrogram),xlab="",sub="",labels=conds_TEs)

4.2 Conclusion

# The Biological QA is good.
# The sequencing depth is good. Also looking at the PCAs we don't have any outliers.

5 Session Info

## R version 4.0.3 (2020-10-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.5 LTS
## 
## Matrix products: default
## BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
##  [1] grid      parallel  stats4    stats     graphics  grDevices utils    
##  [8] datasets  methods   base     
## 
## other attached packages:
##  [1] vsn_3.58.0                  tximport_1.18.0            
##  [3] forcats_0.5.1               stringr_1.4.0              
##  [5] dplyr_1.0.4                 purrr_0.3.4                
##  [7] readr_1.4.0                 tidyr_1.1.3                
##  [9] tibble_3.1.0                tidyverse_1.3.0            
## [11] plotly_4.9.3                pander_0.6.3               
## [13] hyperSpec_0.99-20201127     xml2_1.3.2                 
## [15] ggplot2_3.3.3               lattice_0.20-41            
## [17] here_1.0.1                  gplots_3.1.1               
## [19] DESeq2_1.30.1               SummarizedExperiment_1.20.0
## [21] Biobase_2.50.0              MatrixGenerics_1.2.1       
## [23] matrixStats_0.58.0          GenomicRanges_1.42.0       
## [25] GenomeInfoDb_1.26.4         IRanges_2.24.1             
## [27] S4Vectors_0.28.1            BiocGenerics_0.36.0        
## [29] data.table_1.14.0          
## 
## loaded via a namespace (and not attached):
##  [1] colorspace_2.0-0       ellipsis_0.3.1         rprojroot_2.0.2       
##  [4] XVector_0.30.0         fs_1.5.0               rstudioapi_0.13       
##  [7] hexbin_1.28.2          farver_2.1.0           affyio_1.60.0         
## [10] bit64_4.0.5            AnnotationDbi_1.52.0   fansi_0.4.2           
## [13] lubridate_1.7.10       splines_4.0.3          cachem_1.0.4          
## [16] geneplotter_1.68.0     knitr_1.31             jsonlite_1.7.2        
## [19] broom_0.7.5            annotate_1.68.0        dbplyr_2.1.0          
## [22] png_0.1-7              BiocManager_1.30.10    compiler_4.0.3        
## [25] httr_1.4.2             backports_1.2.1        assertthat_0.2.1      
## [28] Matrix_1.3-2           fastmap_1.1.0          lazyeval_0.2.2        
## [31] limma_3.46.0           cli_2.3.1              htmltools_0.5.1.1     
## [34] tools_4.0.3            affy_1.68.0            gtable_0.3.0          
## [37] glue_1.4.2             GenomeInfoDbData_1.2.4 Rcpp_1.0.6            
## [40] cellranger_1.1.0       jquerylib_0.1.3        vctrs_0.3.6           
## [43] preprocessCore_1.52.1  debugme_1.1.0          crosstalk_1.1.1       
## [46] xfun_0.21              testthat_3.0.2         rvest_1.0.0           
## [49] lifecycle_1.0.0        gtools_3.8.2           XML_3.99-0.5          
## [52] zlibbioc_1.36.0        scales_1.1.1           hms_1.0.0             
## [55] RColorBrewer_1.1-2     yaml_2.2.1             memoise_2.0.0         
## [58] sass_0.3.1             latticeExtra_0.6-29    stringi_1.5.3         
## [61] RSQLite_2.2.4          highr_0.8              genefilter_1.72.1     
## [64] caTools_1.18.1         BiocParallel_1.24.1    rlang_0.4.10          
## [67] pkgconfig_2.0.3        bitops_1.0-6           evaluate_0.14         
## [70] labeling_0.4.2         htmlwidgets_1.5.3      bit_4.0.4             
## [73] tidyselect_1.1.0       magrittr_2.0.1         R6_2.5.0              
## [76] generics_0.1.0         DelayedArray_0.16.2    DBI_1.1.1             
## [79] pillar_1.5.1           haven_2.3.1            withr_2.4.1           
## [82] survival_3.2-7         RCurl_1.98-1.2         modelr_0.1.8          
## [85] crayon_1.4.1           KernSmooth_2.23-18     utf8_1.2.1            
## [88] rmarkdown_2.7          jpeg_0.1-8.1           locfit_1.5-9.4        
## [91] readxl_1.3.1           blob_1.2.1             reprex_1.0.0          
## [94] digest_0.6.27          xtable_1.8-4           munsell_0.5.0         
## [97] viridisLite_0.3.0      bslib_0.2.4